Foveal Curvature and Its Associations in UK Biobank Participants

Purpose To examine whether sociodemographic, and ocular factors relate to optical coherence tomography (OCT)–derived foveal curvature (FC) in healthy individuals. Methods We developed a deep learning model to quantify OCT-derived FC from 63,939 participants (age range, 39–70 years). Associations of FC with sociodemographic, and ocular factors were obtained using multilevel regression analysis (to allow for right and left eyes) adjusting for age, sex, ethnicity, height (model 1), visual acuity, spherical equivalent, corneal astigmatism, center point retinal thickness (CPRT), intraocular pressure (model 2), deprivation (Townsend index), higher education, annual income, and birth order (model 3). Fovea curvature was modeled as a z-score. Results Males had on average steeper FC (0.077; 95% confidence interval [CI] 0.077–0.078) than females (0.068; 95% CI 0.068–0.069). Compared with whites, non-white individuals showed flatter FC, particularly those of black ethnicity. In black males, −0.80 standard deviation (SD) change when compared with whites (95% CI −0.89, −0.71; P 5.2e10−68). In black females, −0.70 SD change when compared with whites (95% CI −0.77, −0.63; p 2.3e10−93). Ocular factors (visual acuity, refractive status, and CPRT) showed a graded inverse association with FC that persisted after adjustment. Macular curvature showed a positive association with FC. Income showed a linear trend increase in males (P for linear trend = 0.005). Conclusions We demonstrate marked differences in FC with ethnicity on the largest cohort studied for this purpose to date. Ocular factors showed a graded association with FC. Implementation of FC quantification in research and on the clinical setting can enhance the understanding of clinical macular phenotypes in health and disease.


Fovea curvature extraction
We assumed that the center of the fovea had the smallest height between the upper and lower boundaries of the segmentation masks. We therefore used height maps generated from the two boundaries to detect the center fovea. First, the ILM and RPE boundaries were extracted by simply tracking the top and the bottom boundary for each generated segmentation mask. By mapping the distance between ILM and RPE boundaries for each location on the 128 B-scans, we obtained a 128x256 height map where each pixel represents the height of ILM-RPE. Next, we applied gaussian blur to the height map (Supplementary Figure 1A). We then binarized the filtered height map using Otsu's algorithm (Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), pp.62-66.). (Supplementary figure  1B). The resulting donut-shaped blob was then detected by a minimum circularity threshold (Supplementary figure 1C). The center of the extracted blob [c_x, c_y] was then determined to be the center of the fovea.

Foveal curvature validation
To validate the automated OCT-derived FC measurements, we divided eyes by FC tertiles. We then selected at random one eye per participant from each tertile, extracted the corresponding B-scan from the selected eye, and created a PSD file which consisted of one B-scan per tertile arranged at random (yielding a total of 10 independent sets with 3 B-scans, one from each curvature tertile). A file containing the B-scan ordered by fovea curvature, per set, was created as a CSV file and used for comparison. Two retina specialists with wide experience in OCT grading (AT, AO-B) were asked to classify each OCT B-scan, from flattest to steepest FC tertile, from each set. The human classification revealed perfect agreement when compared with tertiles derived from the automated quantification.

Supplementary Figure 2.
Representative example of polynomial fit to macular curvature. A convex macular curvature (left) and a concave macular curvature (right) are clearly evidenced on the image.

Supplementary table 3. Sensitivity analysis. Females.
Multilevel models after exclusion of individuals with spherical equivalen refraction < -6 D and > + 6 D, visual acuity < 80 ETDRS letters (worse than 6/7.5 Snellen, or worse than 0.1 logMAR equivalent). Model 1 adjusts for age, ethnicity, and height as fixed effects with a random effect per person to allow for right and left eye measurements. Model 2 extends model 1 by adjusting for visual acuity, spherical equivalent, corneal astigmatism, macula curvature, center point retinal thickness and fluid intelligence. Model 3 further adjusts for annual income, and birth order.

Supplementary Table 4. Sensitivity analysis. Males.
Multilevel models after exclusion of individuals with spherical equivalen refraction < -6 D and > + 6 D, visual acuity < 80 ETDRS letters (worse than 6/7.5 Snellen, or worse than 0.1 logMAR equivalent). Model 1 adjusts for age, ethnicity, and height as fixed effects with a random effect per person to allow for right and left eye measurements. Model 2 extends model 1 by adjusting for visual acuity, spherical equivalent, corneal astigmatism, macula curvature, center point retinal thickness and fluid intelligence. Model 3 further adjusts for annual income, and birth order.  Figure 4. Adjusted mean centre point foveal thickness by deciles of age broken down by sex. Adjusted means (Solid black dots), 95% confidence intervals (Vertical solid lines) and regression line (Dotted line) are from a multilevel model allowing for age, height, ethnicity and UK Biobank centre as fixed effects, and repeated foveal curvature measurement for each person.